For 2D upsampling, some works [1]}, [2]}, [3]}, [4]} used interpolated low resolution (ILR) images, while other works [5]}, [6]}, [7]}, [8]} used the efficient sub-pixel convolutional neural network (ESPCN) introduced in [9]}. Similar to the latter approaches [5]}, [6]}, [7]}, [8]}, we employ the sub-pixel convolutional layer of Shi et al. [9]}. Different from these related works [5]}, [6]}, [7]}, [8]}, we add a convolutional block after the sub-pixel convolutional layer, in order to enhance the HR output image. Furthermore, we propose a novel loss function for our CNN model. Instead of computing the loss between the output image and the ground-truth high-resolution image, we also compute the loss between the intermediate image given by the sub-pixel convolutional layer and the high-resolution image. This forces our neural network to learn a better intermediate representation, increasing its performance.
